Overview

Dataset statistics

Number of variables16
Number of observations1097813
Missing cells190924
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory134.0 MiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical4

Alerts

time has a high cardinality: 45063 distinct values High cardinality
gameId is highly correlated with teamHigh correlation
frameId is highly correlated with s and 1 other fieldsHigh correlation
s is highly correlated with disHigh correlation
dis is highly correlated with sHigh correlation
team is highly correlated with gameIdHigh correlation
nflId has 47731 (4.3%) missing values Missing
jerseyNumber has 47731 (4.3%) missing values Missing
o has 47731 (4.3%) missing values Missing
dir has 47731 (4.3%) missing values Missing
s has 68859 (6.3%) zeros Zeros
a has 64207 (5.8%) zeros Zeros
dis has 69209 (6.3%) zeros Zeros

Reproduction

Analysis started2022-11-02 14:54:51.616857
Analysis finished2022-11-02 14:56:29.754358
Duration1 minute and 38.14 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021100993
Minimum2021100700
Maximum2021101100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:29.802086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2021100700
5-th percentile2021100700
Q12021101002
median2021101007
Q32021101011
95-th percentile2021101100
Maximum2021101100
Range400
Interquartile range (IQR)9

Descriptive statistics

Standard deviation80.74804157
Coefficient of variation (CV)3.995250204 × 10-8
Kurtosis8.455752025
Mean2021100993
Median Absolute Deviation (MAD)4
Skewness-2.924403412
Sum2.218790945 × 1015
Variance6520.246218
MonotonicityIncreasing
2022-11-02T11:56:30.060299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
202110101386664
 
7.9%
202110100885606
 
7.8%
202110110078108
 
7.1%
202110100076889
 
7.0%
202110100774704
 
6.8%
202110100273370
 
6.7%
202110070070748
 
6.4%
202110100170472
 
6.4%
202110100967229
 
6.1%
202110101266493
 
6.1%
Other values (6)347530
31.7%
ValueCountFrequency (%)
202110070070748
6.4%
202110100076889
7.0%
202110100170472
6.4%
202110100273370
6.7%
202110100351612
4.7%
202110100461686
5.6%
202110100565895
6.0%
202110100655453
5.1%
202110100774704
6.8%
202110100885606
7.8%
ValueCountFrequency (%)
202110110078108
7.1%
202110101386664
7.9%
202110101266493
6.1%
202110101162813
5.7%
202110101050071
4.6%
202110100967229
6.1%
202110100885606
7.8%
202110100774704
6.8%
202110100655453
5.1%
202110100565895
6.0%

playId
Real number (ℝ≥0)

Distinct978
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2143.464059
Minimum54
Maximum4597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:30.180906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile233
Q11117
median2176
Q33157
95-th percentile3956
Maximum4597
Range4543
Interquartile range (IQR)2040

Descriptive statistics

Standard deviation1187.342934
Coefficient of variation (CV)0.5539364792
Kurtosis-1.132267128
Mean2143.464059
Median Absolute Deviation (MAD)1019
Skewness-0.03467026475
Sum2353122709
Variance1409783.243
MonotonicityNot monotonic
2022-11-02T11:56:30.303082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25994048
 
0.4%
853910
 
0.4%
25143818
 
0.3%
22533427
 
0.3%
30953289
 
0.3%
7153289
 
0.3%
16023266
 
0.3%
38893128
 
0.3%
26413105
 
0.3%
26683059
 
0.3%
Other values (968)1063474
96.9%
ValueCountFrequency (%)
54828
 
0.1%
55897
 
0.1%
621633
0.1%
631242
 
0.1%
772507
0.2%
83782
 
0.1%
853910
0.4%
861403
 
0.1%
951081
 
0.1%
97805
 
0.1%
ValueCountFrequency (%)
45971403
0.1%
45751012
0.1%
45531058
0.1%
4496828
0.1%
4451667
0.1%
4427920
0.1%
4403851
0.1%
4401874
0.1%
4371667
0.1%
4355828
0.1%

nflId
Real number (ℝ≥0)

MISSING

Distinct1161
Distinct (%)0.1%
Missing47731
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean45721.46635
Minimum25511
Maximum53991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:30.430905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25511
5-th percentile37139
Q142429
median45395
Q348220
95-th percentile53481
Maximum53991
Range28480
Interquartile range (IQR)5791

Descriptive statistics

Standard deviation5050.466699
Coefficient of variation (CV)0.1104616081
Kurtosis-0.121562387
Mean45721.46635
Median Absolute Deviation (MAD)2952
Skewness-0.178522697
Sum4.801128883 × 1010
Variance25507213.88
MonotonicityNot monotonic
2022-11-02T11:56:30.551796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
433672671
 
0.2%
534922671
 
0.2%
525042671
 
0.2%
401072671
 
0.2%
401662671
 
0.2%
536552671
 
0.2%
461522671
 
0.2%
460852671
 
0.2%
448392671
 
0.2%
448222671
 
0.2%
Other values (1151)1023372
93.2%
(Missing)47731
 
4.3%
ValueCountFrequency (%)
255111431
0.1%
28963858
0.1%
29550893
0.1%
298511517
0.1%
30842401
 
< 0.1%
308691298
0.1%
330841695
0.2%
331071778
0.2%
33130520
 
< 0.1%
331311032
0.1%
ValueCountFrequency (%)
53991128
 
< 0.1%
53957932
0.1%
539531374
0.1%
53946135
 
< 0.1%
53900449
 
< 0.1%
53876284
 
< 0.1%
5381936
 
< 0.1%
53687976
0.1%
53674798
0.1%
5366845
 
< 0.1%

frameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct124
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.83895162
Minimum1
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:30.683774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile53
Maximum124
Range123
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.06365415
Coefficient of variation (CV)0.6738406287
Kurtosis2.064723907
Mean23.83895162
Median Absolute Deviation (MAD)11
Skewness1.040341615
Sum26170711
Variance258.0409847
MonotonicityNot monotonic
2022-11-02T11:56:30.816791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125484
 
2.3%
1425484
 
2.3%
2425484
 
2.3%
2325484
 
2.3%
2225484
 
2.3%
2125484
 
2.3%
2025484
 
2.3%
1925484
 
2.3%
1825484
 
2.3%
1725484
 
2.3%
Other values (114)842973
76.8%
ValueCountFrequency (%)
125484
2.3%
225484
2.3%
325484
2.3%
425484
2.3%
525484
2.3%
625484
2.3%
725484
2.3%
825484
2.3%
925484
2.3%
1025484
2.3%
ValueCountFrequency (%)
12423
 
< 0.1%
12346
< 0.1%
12246
< 0.1%
12146
< 0.1%
12069
< 0.1%
11969
< 0.1%
11869
< 0.1%
11769
< 0.1%
11669
< 0.1%
11569
< 0.1%

time
Categorical

HIGH CARDINALITY

Distinct45063
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2021-10-10T17:24:24.300
 
69
2021-10-10T18:52:51.200
 
69
2021-10-10T18:52:51.400
 
69
2021-10-10T18:52:51.500
 
69
2021-10-10T18:52:51.600
 
69
Other values (45058)
1097468 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters25249699
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row2021-10-08T00:23:33.900
2nd row2021-10-08T00:23:34.000
3rd row2021-10-08T00:23:34.100
4th row2021-10-08T00:23:34.200
5th row2021-10-08T00:23:34.300

Common Values

ValueCountFrequency (%)
2021-10-10T17:24:24.30069
 
< 0.1%
2021-10-10T18:52:51.20069
 
< 0.1%
2021-10-10T18:52:51.40069
 
< 0.1%
2021-10-10T18:52:51.50069
 
< 0.1%
2021-10-10T18:52:51.60069
 
< 0.1%
2021-10-10T19:56:43.10069
 
< 0.1%
2021-10-10T19:17:14.20069
 
< 0.1%
2021-10-10T19:17:14.10069
 
< 0.1%
2021-10-10T19:17:14.00069
 
< 0.1%
2021-10-10T19:17:13.90069
 
< 0.1%
Other values (45053)1097123
99.9%

Length

2022-11-02T11:56:30.938002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-10-10t17:24:24.30069
 
< 0.1%
2021-10-10t18:15:54.00069
 
< 0.1%
2021-10-10t18:10:04.50069
 
< 0.1%
2021-10-10t17:44:14.60069
 
< 0.1%
2021-10-10t17:44:14.70069
 
< 0.1%
2021-10-10t17:44:14.80069
 
< 0.1%
2021-10-10t17:44:14.90069
 
< 0.1%
2021-10-10t17:44:15.00069
 
< 0.1%
2021-10-10t19:17:16.30069
 
< 0.1%
2021-10-10t19:17:16.20069
 
< 0.1%
Other values (45053)1097123
99.9%

Most occurring characters

ValueCountFrequency (%)
06429738
25.5%
14713919
18.7%
23426195
13.6%
-2195626
 
8.7%
:2195626
 
8.7%
T1097813
 
4.3%
.1097813
 
4.3%
3755877
 
3.0%
4733425
 
2.9%
5682457
 
2.7%
Other values (4)1921210
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18662821
73.9%
Other Punctuation3293439
 
13.0%
Dash Punctuation2195626
 
8.7%
Uppercase Letter1097813
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06429738
34.5%
14713919
25.3%
23426195
18.4%
3755877
 
4.1%
4733425
 
3.9%
5682457
 
3.7%
8549449
 
2.9%
9507448
 
2.7%
7495324
 
2.7%
6368989
 
2.0%
Other Punctuation
ValueCountFrequency (%)
:2195626
66.7%
.1097813
33.3%
Dash Punctuation
ValueCountFrequency (%)
-2195626
100.0%
Uppercase Letter
ValueCountFrequency (%)
T1097813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24151886
95.7%
Latin1097813
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
06429738
26.6%
14713919
19.5%
23426195
14.2%
-2195626
 
9.1%
:2195626
 
9.1%
.1097813
 
4.5%
3755877
 
3.1%
4733425
 
3.0%
5682457
 
2.8%
8549449
 
2.3%
Other values (3)1371761
 
5.7%
Latin
ValueCountFrequency (%)
T1097813
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25249699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06429738
25.5%
14713919
18.7%
23426195
13.6%
-2195626
 
8.7%
:2195626
 
8.7%
T1097813
 
4.3%
.1097813
 
4.3%
3755877
 
3.0%
4733425
 
2.9%
5682457
 
2.7%
Other values (4)1921210
 
7.6%

jerseyNumber
Real number (ℝ≥0)

MISSING

Distinct98
Distinct (%)< 0.1%
Missing47731
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean50.15997798
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:31.051085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q123
median52
Q376
95-th percentile96
Maximum99
Range98
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.82252554
Coefficient of variation (CV)0.5945482184
Kurtosis-1.317059502
Mean50.15997798
Median Absolute Deviation (MAD)27
Skewness0.02384140107
Sum52672090
Variance889.3830298
MonotonicityNot monotonic
2022-11-02T11:56:31.184354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2321810
 
2.0%
2420766
 
1.9%
9720291
 
1.8%
2120005
 
1.8%
1118360
 
1.7%
5517503
 
1.6%
7216670
 
1.5%
9116208
 
1.5%
9916088
 
1.5%
216010
 
1.5%
Other values (88)866371
78.9%
(Missing)47731
 
4.3%
ValueCountFrequency (%)
114121
1.3%
216010
1.5%
35823
 
0.5%
413474
1.2%
59312
0.8%
67360
0.7%
76763
0.6%
810217
0.9%
97222
0.7%
1010604
1.0%
ValueCountFrequency (%)
9916088
1.5%
9815556
1.4%
9720291
1.8%
9610903
1.0%
958368
0.8%
9415665
1.4%
9310761
1.0%
925185
 
0.5%
9116208
1.5%
9014426
1.3%

team
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
football
 
47731
KC
 
41448
BUF
 
41448
WAS
 
40942
NO
 
40942
Other values (28)
885302 

Length

Max length8
Median length3
Mean length2.975350082
Min length2

Characters and Unicode

Total characters3266378
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA
2nd rowLA
3rd rowLA
4th rowLA
5th rowLA

Common Values

ValueCountFrequency (%)
football47731
 
4.3%
KC41448
 
3.8%
BUF41448
 
3.8%
WAS40942
 
3.7%
NO40942
 
3.7%
BAL37356
 
3.4%
IND37356
 
3.4%
ATL36773
 
3.3%
NYJ36773
 
3.3%
MIA35728
 
3.3%
Other values (23)701316
63.9%

Length

2022-11-02T11:56:31.303645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
football47731
 
4.3%
kc41448
 
3.8%
buf41448
 
3.8%
was40942
 
3.7%
no40942
 
3.7%
bal37356
 
3.4%
ind37356
 
3.4%
atl36773
 
3.3%
nyj36773
 
3.3%
mia35728
 
3.3%
Other values (23)701316
63.9%

Most occurring characters

ValueCountFrequency (%)
A375672
 
11.5%
N294184
 
9.0%
I253902
 
7.8%
L228019
 
7.0%
C198495
 
6.1%
E178211
 
5.5%
T160039
 
4.9%
B149622
 
4.6%
D127193
 
3.9%
S104819
 
3.2%
Other values (20)1196222
36.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2884530
88.3%
Lowercase Letter381848
 
11.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A375672
13.0%
N294184
 
10.2%
I253902
 
8.8%
L228019
 
7.9%
C198495
 
6.9%
E178211
 
6.2%
T160039
 
5.5%
B149622
 
5.2%
D127193
 
4.4%
S104819
 
3.6%
Other values (14)814374
28.2%
Lowercase Letter
ValueCountFrequency (%)
l95462
25.0%
o95462
25.0%
f47731
12.5%
a47731
12.5%
b47731
12.5%
t47731
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin3266378
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A375672
 
11.5%
N294184
 
9.0%
I253902
 
7.8%
L228019
 
7.0%
C198495
 
6.1%
E178211
 
5.5%
T160039
 
4.9%
B149622
 
4.6%
D127193
 
3.9%
S104819
 
3.2%
Other values (20)1196222
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3266378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A375672
 
11.5%
N294184
 
9.0%
I253902
 
7.8%
L228019
 
7.0%
C198495
 
6.1%
E178211
 
5.5%
T160039
 
4.9%
B149622
 
4.6%
D127193
 
3.9%
S104819
 
3.2%
Other values (20)1196222
36.6%

playDirection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
left
560878 
right
536935 

Length

Max length5
Median length4
Mean length4.489095137
Min length4

Characters and Unicode

Total characters4928187
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowright
4th rowright
5th rowright

Common Values

ValueCountFrequency (%)
left560878
51.1%
right536935
48.9%

Length

2022-11-02T11:56:31.398435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:56:31.492576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
left560878
51.1%
right536935
48.9%

Most occurring characters

ValueCountFrequency (%)
t1097813
22.3%
l560878
11.4%
e560878
11.4%
f560878
11.4%
r536935
10.9%
i536935
10.9%
g536935
10.9%
h536935
10.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4928187
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1097813
22.3%
l560878
11.4%
e560878
11.4%
f560878
11.4%
r536935
10.9%
i536935
10.9%
g536935
10.9%
h536935
10.9%

Most occurring scripts

ValueCountFrequency (%)
Latin4928187
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1097813
22.3%
l560878
11.4%
e560878
11.4%
f560878
11.4%
r536935
10.9%
i536935
10.9%
g536935
10.9%
h536935
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4928187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t1097813
22.3%
l560878
11.4%
e560878
11.4%
f560878
11.4%
r536935
10.9%
i536935
10.9%
g536935
10.9%
h536935
10.9%

x
Real number (ℝ)

Distinct11786
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.77382293
Minimum-1.72
Maximum119.57
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)< 0.1%
Memory size8.4 MiB
2022-11-02T11:56:31.587551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.72
5-th percentile20.33
Q140.91
median59.38
Q378.42
95-th percentile100.41
Maximum119.57
Range121.29
Interquartile range (IQR)37.51

Descriptive statistics

Standard deviation24.28968786
Coefficient of variation (CV)0.4063599527
Kurtosis-0.7464431436
Mean59.77382293
Median Absolute Deviation (MAD)18.74
Skewness0.05042027758
Sum65620479.87
Variance589.9889364
MonotonicityNot monotonic
2022-11-02T11:56:31.719538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.67208
 
< 0.1%
66.24203
 
< 0.1%
41.63202
 
< 0.1%
43.37198
 
< 0.1%
63.67195
 
< 0.1%
40.6194
 
< 0.1%
48.42194
 
< 0.1%
45.59193
 
< 0.1%
44.57193
 
< 0.1%
54.65191
 
< 0.1%
Other values (11776)1095842
99.8%
ValueCountFrequency (%)
-1.721
< 0.1%
-1.71
< 0.1%
-1.691
< 0.1%
-1.661
< 0.1%
-1.591
< 0.1%
-1.51
< 0.1%
-1.381
< 0.1%
-1.231
< 0.1%
-1.071
< 0.1%
-0.881
< 0.1%
ValueCountFrequency (%)
119.571
< 0.1%
119.541
< 0.1%
119.51
< 0.1%
119.481
< 0.1%
119.41
< 0.1%
119.351
< 0.1%
119.291
< 0.1%
119.261
< 0.1%
119.171
< 0.1%
119.081
< 0.1%

y
Real number (ℝ)

Distinct5424
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.78455619
Minimum-2.02
Maximum56.59
Zeros0
Zeros (%)0.0%
Negative23
Negative (%)< 0.1%
Memory size8.4 MiB
2022-11-02T11:56:31.850500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.02
5-th percentile11.71
Q122
median26.8
Q331.63
95-th percentile41.73
Maximum56.59
Range58.61
Interquartile range (IQR)9.63

Descriptive statistics

Standard deviation8.312595357
Coefficient of variation (CV)0.3103503114
Kurtosis0.3263039226
Mean26.78455619
Median Absolute Deviation (MAD)4.81
Skewness-0.01627168227
Sum29404433.99
Variance69.09924156
MonotonicityNot monotonic
2022-11-02T11:56:31.971754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.751105
 
0.1%
23.751101
 
0.1%
29.811089
 
0.1%
29.831088
 
0.1%
23.741082
 
0.1%
29.81076
 
0.1%
23.821071
 
0.1%
23.71067
 
0.1%
23.731054
 
0.1%
29.791053
 
0.1%
Other values (5414)1087027
99.0%
ValueCountFrequency (%)
-2.021
< 0.1%
-2.011
< 0.1%
-21
< 0.1%
-1.971
< 0.1%
-1.951
< 0.1%
-1.911
< 0.1%
-1.871
< 0.1%
-1.811
< 0.1%
-1.781
< 0.1%
-1.681
< 0.1%
ValueCountFrequency (%)
56.591
< 0.1%
56.511
< 0.1%
56.411
< 0.1%
56.311
< 0.1%
56.21
< 0.1%
56.081
< 0.1%
55.991
< 0.1%
55.951
< 0.1%
55.821
< 0.1%
55.751
< 0.1%

s
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2157
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.598595617
Minimum0
Maximum29.34
Zeros68859
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:32.104302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.76
median2.14
Q33.83
95-th percentile6.83
Maximum29.34
Range29.34
Interquartile range (IQR)3.07

Descriptive statistics

Standard deviation2.410041143
Coefficient of variation (CV)0.9274398555
Kurtosis14.17000886
Mean2.598595617
Median Absolute Deviation (MAD)1.5
Skewness2.341926989
Sum2852772.05
Variance5.808298313
MonotonicityNot monotonic
2022-11-02T11:56:32.225325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
068859
 
6.3%
0.0116548
 
1.5%
0.029333
 
0.9%
0.037047
 
0.6%
0.045777
 
0.5%
0.055107
 
0.5%
0.064728
 
0.4%
0.074231
 
0.4%
0.083909
 
0.4%
0.093874
 
0.4%
Other values (2147)968400
88.2%
ValueCountFrequency (%)
068859
6.3%
0.0116548
 
1.5%
0.029333
 
0.9%
0.037047
 
0.6%
0.045777
 
0.5%
0.055107
 
0.5%
0.064728
 
0.4%
0.074231
 
0.4%
0.083909
 
0.4%
0.093874
 
0.4%
ValueCountFrequency (%)
29.341
< 0.1%
291
< 0.1%
28.481
< 0.1%
27.391
< 0.1%
27.281
< 0.1%
27.241
< 0.1%
27.181
< 0.1%
27.121
< 0.1%
27.071
< 0.1%
27.051
< 0.1%

a
Real number (ℝ≥0)

ZEROS

Distinct1587
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.782808083
Minimum0
Maximum27.26
Zeros64207
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:32.353267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.71
median1.53
Q32.57
95-th percentile4.44
Maximum27.26
Range27.26
Interquartile range (IQR)1.86

Descriptive statistics

Standard deviation1.435226143
Coefficient of variation (CV)0.8050368163
Kurtosis6.592413885
Mean1.782808083
Median Absolute Deviation (MAD)0.91
Skewness1.472769507
Sum1957189.89
Variance2.059874082
MonotonicityNot monotonic
2022-11-02T11:56:32.478560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
064207
 
5.8%
0.0112817
 
1.2%
0.027246
 
0.7%
0.035485
 
0.5%
0.044643
 
0.4%
0.053820
 
0.3%
1.333525
 
0.3%
1.23521
 
0.3%
1.043506
 
0.3%
1.123503
 
0.3%
Other values (1577)985540
89.8%
ValueCountFrequency (%)
064207
5.8%
0.0112817
 
1.2%
0.027246
 
0.7%
0.035485
 
0.5%
0.044643
 
0.4%
0.053820
 
0.3%
0.063358
 
0.3%
0.073045
 
0.3%
0.082780
 
0.3%
0.092620
 
0.2%
ValueCountFrequency (%)
27.261
< 0.1%
26.431
< 0.1%
26.251
< 0.1%
26.141
< 0.1%
25.481
< 0.1%
24.281
< 0.1%
24.151
< 0.1%
23.741
< 0.1%
23.441
< 0.1%
23.341
< 0.1%

dis
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct538
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2631515294
Minimum0
Maximum7.1
Zeros69209
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:32.766255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.22
Q30.38
95-th percentile0.68
Maximum7.1
Range7.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2570572949
Coefficient of variation (CV)0.9768413484
Kurtosis47.47513465
Mean0.2631515294
Median Absolute Deviation (MAD)0.15
Skewness4.09396423
Sum288891.17
Variance0.06607845284
MonotonicityNot monotonic
2022-11-02T11:56:32.892679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
069209
 
6.3%
0.0157954
 
5.3%
0.0233452
 
3.0%
0.0325844
 
2.4%
0.0423066
 
2.1%
0.0521323
 
1.9%
0.2120481
 
1.9%
0.0620397
 
1.9%
0.220209
 
1.8%
0.1920182
 
1.8%
Other values (528)785696
71.6%
ValueCountFrequency (%)
069209
6.3%
0.0157954
5.3%
0.0233452
3.0%
0.0325844
 
2.4%
0.0423066
 
2.1%
0.0521323
 
1.9%
0.0620397
 
1.9%
0.0720044
 
1.8%
0.0819495
 
1.8%
0.0919363
 
1.8%
ValueCountFrequency (%)
7.11
< 0.1%
6.911
< 0.1%
6.791
< 0.1%
6.631
< 0.1%
6.531
< 0.1%
6.491
< 0.1%
6.431
< 0.1%
6.171
< 0.1%
6.011
< 0.1%
62
< 0.1%

o
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.4%
Missing47731
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean180.8714058
Minimum0
Maximum360
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:33.028249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.08
Q190.33
median179.93
Q3269.79
95-th percentile331.08
Maximum360
Range360
Interquartile range (IQR)179.46

Descriptive statistics

Standard deviation99.23953905
Coefficient of variation (CV)0.5486745604
Kurtosis-1.356028944
Mean180.8714058
Median Absolute Deviation (MAD)89.73
Skewness-0.0003806155838
Sum189929807.5
Variance9848.486111
MonotonicityNot monotonic
2022-11-02T11:56:33.152730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90501
 
< 0.1%
264.52111
 
< 0.1%
265.75110
 
< 0.1%
269.43103
 
< 0.1%
92.08103
 
< 0.1%
265.08103
 
< 0.1%
267.47101
 
< 0.1%
89.25101
 
< 0.1%
85.9100
 
< 0.1%
269.41100
 
< 0.1%
Other values (35991)1048649
95.5%
(Missing)47731
 
4.3%
ValueCountFrequency (%)
017
< 0.1%
0.0112
< 0.1%
0.0214
< 0.1%
0.0312
< 0.1%
0.0417
< 0.1%
0.0516
< 0.1%
0.0615
< 0.1%
0.0714
< 0.1%
0.0827
< 0.1%
0.0914
< 0.1%
ValueCountFrequency (%)
36014
< 0.1%
359.9928
< 0.1%
359.9811
 
< 0.1%
359.9728
< 0.1%
359.9619
< 0.1%
359.9517
< 0.1%
359.9415
< 0.1%
359.9326
< 0.1%
359.9218
< 0.1%
359.9115
< 0.1%

dir
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.4%
Missing47731
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean180.8815035
Minimum0
Maximum360
Zeros28
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2022-11-02T11:56:33.286287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.47
Q190.93
median180.04
Q3271.18
95-th percentile337.68
Maximum360
Range360
Interquartile range (IQR)180.25

Descriptive statistics

Standard deviation101.4632771
Coefficient of variation (CV)0.5609378245
Kurtosis-1.284141938
Mean180.8815035
Median Absolute Deviation (MAD)90.13
Skewness-0.004000071251
Sum189940411
Variance10294.79659
MonotonicityNot monotonic
2022-11-02T11:56:33.412003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
273.2272
 
< 0.1%
262.972
 
< 0.1%
88.2672
 
< 0.1%
272.4171
 
< 0.1%
95.671
 
< 0.1%
96.170
 
< 0.1%
271.9670
 
< 0.1%
274.5170
 
< 0.1%
261.3269
 
< 0.1%
272.6169
 
< 0.1%
Other values (35991)1049376
95.6%
(Missing)47731
 
4.3%
ValueCountFrequency (%)
028
< 0.1%
0.0130
< 0.1%
0.0222
< 0.1%
0.0327
< 0.1%
0.0425
< 0.1%
0.0527
< 0.1%
0.0628
< 0.1%
0.0716
< 0.1%
0.0830
< 0.1%
0.0933
< 0.1%
ValueCountFrequency (%)
36010
 
< 0.1%
359.9920
< 0.1%
359.9830
< 0.1%
359.9721
< 0.1%
359.9618
< 0.1%
359.9520
< 0.1%
359.9426
< 0.1%
359.9330
< 0.1%
359.9226
< 0.1%
359.9126
< 0.1%

event
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
None
1013955 
ball_snap
 
25415
pass_forward
 
22747
autoevent_ballsnap
 
11569
autoevent_passforward
 
11178
Other values (14)
 
12949

Length

Max length25
Median length4
Mean length4.676771909
Min length3

Characters and Unicode

Total characters5134221
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None1013955
92.4%
ball_snap25415
 
2.3%
pass_forward22747
 
2.1%
autoevent_ballsnap11569
 
1.1%
autoevent_passforward11178
 
1.0%
play_action6049
 
0.6%
run1403
 
0.1%
qb_sack1219
 
0.1%
pass_arrived1058
 
0.1%
shift690
 
0.1%
Other values (9)2530
 
0.2%

Length

2022-11-02T11:56:33.537075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none1013955
92.4%
ball_snap25415
 
2.3%
pass_forward22747
 
2.1%
autoevent_ballsnap11569
 
1.1%
autoevent_passforward11178
 
1.0%
play_action6049
 
0.6%
run1403
 
0.1%
qb_sack1219
 
0.1%
pass_arrived1058
 
0.1%
shift690
 
0.1%
Other values (9)2530
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n1084818
21.1%
o1079114
21.0%
e1064164
20.7%
N1013955
19.7%
a182827
 
3.6%
s112056
 
2.2%
_82593
 
1.6%
p80891
 
1.6%
l80408
 
1.6%
r72979
 
1.4%
Other values (15)280416
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4037673
78.6%
Uppercase Letter1013955
 
19.7%
Connector Punctuation82593
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1084818
26.9%
o1079114
26.7%
e1064164
26.4%
a182827
 
4.5%
s112056
 
2.8%
p80891
 
2.0%
l80408
 
2.0%
r72979
 
1.8%
t57224
 
1.4%
b38341
 
0.9%
Other values (13)184851
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
N1013955
100.0%
Connector Punctuation
ValueCountFrequency (%)
_82593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5051628
98.4%
Common82593
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1084818
21.5%
o1079114
21.4%
e1064164
21.1%
N1013955
20.1%
a182827
 
3.6%
s112056
 
2.2%
p80891
 
1.6%
l80408
 
1.6%
r72979
 
1.4%
t57224
 
1.1%
Other values (14)223192
 
4.4%
Common
ValueCountFrequency (%)
_82593
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5134221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1084818
21.1%
o1079114
21.0%
e1064164
20.7%
N1013955
19.7%
a182827
 
3.6%
s112056
 
2.2%
_82593
 
1.6%
p80891
 
1.6%
l80408
 
1.6%
r72979
 
1.4%
Other values (15)280416
 
5.5%

Interactions

2022-11-02T11:56:21.459051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:44.022270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:47.405418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:50.633627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:54.122144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:57.453187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:00.907992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:04.261748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:07.576383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:11.089371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:14.497557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:17.873852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:21.765876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:44.309952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:47.674125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:51.078357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:54.400484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:57.735362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:01.194947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:04.548196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:07.865862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:11.381275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:14.794904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:18.324759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:22.042602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:44.585423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:47.934193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:51.340850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:54.671665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:58.000690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:01.460521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:04.813538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:08.142759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:11.659621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:15.076610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:18.606799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:22.329541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:44.862310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:48.206993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:51.617207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:54.947583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:58.274675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:01.737906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:05.083187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:08.413982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:11.948098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:15.354286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:18.890764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:22.613450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:45.142021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:48.473273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:51.890461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:55.232910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:58.541430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:02.016342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:05.358717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:08.696224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:12.242702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:15.638222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:19.176085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:22.895106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:45.420525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:48.741746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:52.163168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:55.510942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:58.809070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:02.290164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:05.635427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:09.128251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:12.521621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:15.912794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:19.455777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:23.184764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:45.704215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:49.013096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:52.436215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:55.788742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:59.080474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:02.571358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:05.902255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:09.407917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:12.798958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:16.196207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:19.739828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:23.469147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:45.988386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:49.280897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:52.721143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:56.063741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:59.354525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:02.848833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:06.179858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:09.675333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:13.080703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:16.475250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:20.021154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:23.752181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:46.267661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:49.545492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:52.999073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:56.339621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:59.626472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:03.136196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:06.459982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:09.956044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:13.355248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:16.761342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:20.306440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:24.040621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:46.549383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:49.813799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:53.289331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:56.613678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:00.074461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:03.416121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:06.737196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:10.231143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:13.634959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:17.026485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:20.596378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:24.318283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:46.839045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:50.086149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:53.562541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:56.892283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:00.347075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:03.701113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:07.015280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:10.517646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:13.916613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:17.311163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:20.872115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:24.602323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:47.125729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:50.360611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:53.842887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:55:57.176517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:00.627422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:03.981207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:07.295080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:10.802320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:14.215493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:17.594753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:56:21.166027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T11:56:33.635323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T11:56:33.787674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T11:56:33.934419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T11:56:34.081503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T11:56:34.216742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-02T11:56:34.326574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T11:56:25.185897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T11:56:26.436061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-02T11:56:28.375358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-02T11:56:29.054740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
020211007009530869.012021-10-08T00:23:33.90077.0LAright22.9333.040.020.020.00102.26129.51None
120211007009530869.022021-10-08T00:23:34.00077.0LAright22.9333.040.010.020.00101.64128.68None
220211007009530869.032021-10-08T00:23:34.10077.0LAright22.9233.050.010.010.01100.73127.26None
320211007009530869.042021-10-08T00:23:34.20077.0LAright22.9233.050.010.010.01100.73130.90None
420211007009530869.052021-10-08T00:23:34.30077.0LAright22.9133.050.010.010.0199.55134.16None
520211007009530869.062021-10-08T00:23:34.40077.0LAright22.9133.050.010.010.0099.55134.37ball_snap
620211007009530869.072021-10-08T00:23:34.50077.0LAright22.9033.050.010.010.0199.55138.57autoevent_ballsnap
720211007009530869.082021-10-08T00:23:34.60077.0LAright22.8933.060.000.000.0298.54144.90None
820211007009530869.092021-10-08T00:23:34.70077.0LAright22.8833.070.000.000.0197.83143.93None
920211007009530869.0102021-10-08T00:23:34.80077.0LAright22.8733.080.000.000.0196.94150.31None

Last rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
109780320211011004401NaN292021-10-12T03:30:05.900NaNfootballright97.0621.911.582.810.14NaNNaNNone
109780420211011004401NaN302021-10-12T03:30:06.000NaNfootballright97.1321.751.802.510.17NaNNaNNone
109780520211011004401NaN312021-10-12T03:30:06.100NaNfootballright97.2321.591.972.160.19NaNNaNNone
109780620211011004401NaN322021-10-12T03:30:06.200NaNfootballright97.3521.432.121.670.20NaNNaNNone
109780720211011004401NaN332021-10-12T03:30:06.300NaNfootballright97.4821.262.231.240.22NaNNaNpass_forward
109780820211011004401NaN342021-10-12T03:30:06.400NaNfootballright100.8019.4221.360.173.80NaNNaNNone
109780920211011004401NaN352021-10-12T03:30:06.500NaNfootballright102.6118.2921.291.132.13NaNNaNNone
109781020211011004401NaN362021-10-12T03:30:06.600NaNfootballright104.4117.1721.132.062.12NaNNaNNone
109781120211011004401NaN372021-10-12T03:30:06.700NaNfootballright106.2016.0620.882.942.10NaNNaNNone
109781220211011004401NaN382021-10-12T03:30:06.800NaNfootballright107.9614.9720.553.712.07NaNNaNNone